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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 6153749, 14 pages
Research Article

Neural Architectures for Correlated Noise Removal in Image Processing

Computer Science Department, Bucharest University of Economics, 010552 Bucharest, Romania

Received 21 January 2016; Accepted 24 March 2016

Academic Editor: Marco Perez-Cisneros

Copyright © 2016 Cătălina Cocianu and Alexandru Stan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The paper proposes a new method that combines the decorrelation and shrinkage techniques to neural network-based approaches for noise removal purposes. The images are represented as sequences of equal sized blocks, each block being distorted by a stationary statistical correlated noise. Some significant amount of the induced noise in the blocks is removed in a preprocessing step, using a decorrelation method combined with a standard shrinkage-based technique. The preprocessing step provides for each initial image a sequence of blocks that are further compressed at a certain rate, each component of the resulting sequence being supplied as inputs to a feed-forward neural architecture . The local memories of the neurons of the layers and are generated through a supervised learning process based on the compressed versions of blocks of the same index value supplied as inputs and the compressed versions of them resulting as the mean of their preprocessed versions. Finally, using the standard decompression technique, the sequence of the decompressed blocks is the cleaned representation of the initial image. The performance of the proposed method is evaluated by a long series of tests, the results being very encouraging as compared to similar developments for noise removal purposes.